{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 推荐系统融合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有特征串联起来，构成RS_Train.csv\n",
    "#RS_Test.csv\n",
    "#为最后推荐系统做准备\n",
    "from __future__ import division\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "from numpy.random import random  \n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 推荐系统的类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RecommonderSystem:\n",
    "  def __init__(self):\n",
    "    # 读入数据做初始化\n",
    "    \n",
    "    #用户和活动新的索引\n",
    "    self.userIndex = pickle.load(open(\"PE_userIndex.pkl\", 'rb'))\n",
    "    self.eventIndex = pickle.load(open(\"PE_eventIndex.pkl\", 'rb'))\n",
    "    self.n_users = len(self.userIndex) #用户数量\n",
    "    self.n_items = len(self.eventIndex) #活动数量\n",
    "    \n",
    "    #用户-活动关系矩阵R\n",
    "    #在train_SVD会重新从文件中读取,二者要求的格式不同，来不及统一了:(\n",
    "    self.userEventScores = sio.mmread(\"PE_userEventScores\").todense()\n",
    "    \n",
    "    #倒排表\n",
    "    ##每个用户参加的事件\n",
    "    self.itemsForUser = pickle.load(open(\"PE_eventsForUser.pkl\", 'rb'))\n",
    "    ##事件参加的用户\n",
    "    self.usersForItem = pickle.load(open(\"PE_usersForEvent.pkl\", 'rb'))\n",
    "    \n",
    "    #基于模型的协同过滤参数初始化,训练\n",
    "    self.init_SVD()\n",
    "    self.train_SVD(trainfile = \"./data/train.csv\")\n",
    "    \n",
    "    #根据用户属性计算出的用户之间的相似度\n",
    "    self.userSimMatrix = sio.mmread(\"US_userSimMatrix\").todense()\n",
    "    \n",
    "    #根据活动属性计算出的活动之间的相似度\n",
    "    self.eventPropSim = sio.mmread(\"EV_eventPropSim\").todense()\n",
    "    self.eventContSim = sio.mmread(\"EV_eventContSim\").todense()\n",
    "    \n",
    "    #每个用户的朋友的数目\n",
    "    self.numFriends = sio.mmread(\"UF_numFriends\")\n",
    "    #用户的每个朋友参加活动的分数对该用户的影响\n",
    "    self.userFriends = sio.mmread(\"UF_userFriends\").todense()\n",
    "    \n",
    "    #活动本身的热度\n",
    "    self.eventPopularity = sio.mmread(\"EA_eventPopularity\").todense()\n",
    "\n",
    "  def init_SVD(self, K=20):\n",
    "    #初始化模型参数（for 基于模型的协同过滤SVD_CF）\n",
    "    self.K = K  \n",
    "    \n",
    "    #init parameters\n",
    "    #bias\n",
    "    self.bi = np.zeros(self.n_items)  \n",
    "    self.bu = np.zeros(self.n_users)  \n",
    "    \n",
    "    #the small matrix\n",
    "    self.P = random((self.n_users,self.K))/10*(np.sqrt(self.K))\n",
    "    self.Q = random((self.K, self.n_items))/10*(np.sqrt(self.K))  \n",
    "                  \n",
    "          \n",
    "  def train_SVD(self,trainfile = './data/train.csv', steps=100,gamma=0.04,Lambda=0.15):\n",
    "    #训练SVD模型（for 基于模型的协同过滤SVD_CF）\n",
    "    #gamma：为学习率\n",
    "    #Lambda：正则参数\n",
    "    \n",
    "    #偷懒了，为了和原来的代码的输入接口一样，直接从训练文件中去读取数据\n",
    "    print (\"SVD Train...\")\n",
    "    ftrain = open(trainfile, 'r')\n",
    "    ftrain.readline()\n",
    "    self.mu = 0.0\n",
    "    n_records = 0\n",
    "    uids = []  #每条记录的用户索引\n",
    "    i_ids = [] #每条记录的item索引\n",
    "    #用户-Item关系矩阵R（内容同userEventScores相同），临时变量，训练完了R不再需要\n",
    "    R = np.zeros((self.n_users, self.n_items))\n",
    "    \n",
    "    for line in ftrain:\n",
    "        cols = line.strip().split(\",\")\n",
    "        u = self.userIndex[cols[0]]  #用户\n",
    "        i = self.eventIndex[cols[1]] #活动\n",
    "        \n",
    "        uids.append(u)\n",
    "        i_ids.append(i)\n",
    "        \n",
    "        R[u,i] = int(cols[4])  #interested\n",
    "        self.mu += R[u,i]\n",
    "        n_records += 1\n",
    "    \n",
    "    ftrain.close()\n",
    "    self.mu /= n_records\n",
    "    \n",
    "    # 请补充完整SVD模型训练过程\n",
    "    #将训练样本打散顺序\n",
    "    for step in range(steps):\n",
    "         print('the ',step,'-th  step is running')  \n",
    "         rmse_sum=0.0 \n",
    "         #将训练样本打散顺序\n",
    "         kk = np.random.permutation(n_records)  \n",
    "         for j in range(n_records):\n",
    "                #每次一个训练样本\n",
    "                index=kk[j]  \n",
    "                u= uids[index]\n",
    "                i= i_ids[index]\n",
    "                \n",
    "                #预测残差\n",
    "                eui=R[u,i]-self.pred_SVD(u,i)  \n",
    "                #残差平方和\n",
    "                rmse_sum+=eui**2  \n",
    "\n",
    "                #随机梯度下降，更新\n",
    "                for k in range(self.K):\n",
    "                    self.P[u,k]+=gamma*(eui*self.Q[k,i]-Lambda*self.P[u,k])  \n",
    "                    self.Q[k,i]+=gamma*(eui*self.P[u,k]-Lambda*self.Q[k,i]) \n",
    "\n",
    "                    self.bu[u]+=gamma*(eui-Lambda*self.bu[u])  \n",
    "                    self.bi[i]+=gamma*(eui-Lambda*self.bi[i])   \n",
    "\n",
    "        #学习率递减\n",
    "         gamma=gamma*0.93  \n",
    "    print (\"SVD trained\")\n",
    "    \n",
    "  def pred_SVD(self, uid, i_id):\n",
    "    #根据当前参数，预测用户uid对Item（i_id）的打分        \n",
    "    ans=self.mu + self.bi[i_id] + self.bu[uid] + np.dot(self.P[uid,:],self.Q[:,i_id])  \n",
    "        \n",
    "    #将打分范围控制在0-1之间\n",
    "    if ans>1:  \n",
    "        return 1  \n",
    "    elif ans<0:  \n",
    "        return 0\n",
    "    return ans  \n",
    "\n",
    "  def sim_cal_UserCF(self, uid1, uid2 ):\n",
    "        #请补充基于用户的协同过滤中的两个用户uid1和uid2之间的相似度（根据两个用户对item打分的相似度）\n",
    "        \"\"\"\n",
    "            思路：遍历itemsForUser 找出用户1和用户2共同参加过的活动的所有集合\n",
    "            先在itemForUser 中找出user1参加过的活动，然后判断user2和user1有没有参加同一个活动\n",
    "            如果有，则集合加一 用dic{\"活动ID\",1}\n",
    "            之后再遍历集合计算相似度\n",
    "        \"\"\" \n",
    "        similarity = 0.0 #相似度，初始为0\n",
    "        eventList = {}\n",
    "        for i in self.itemsForUser[uid1]: #找出用户1参加的所有活动\n",
    "            if i in self.itemsForUser[uid2]: #俩个用户参加了同一个活动\n",
    "                eventList[i]=1 #加入集合\n",
    "        \n",
    "        n = len(eventList)\n",
    "        if(n==0):\n",
    "            return similarity\n",
    "       \n",
    "        #用户1 打过分的所有有效item \n",
    "        s1 = np.array([self.userEventScores[uid1,item] for item in eventList])\n",
    "        \n",
    "         #用户2 打过分的所有有效item \n",
    "        s2 = np.array([self.userEventScores[uid2,item] for item in eventList])\n",
    "        \n",
    "        #s1和s2相当于一个矩阵了可以使用算法了\n",
    "        sum1 = np.sum(s1) #s1的总分数\n",
    "        sum2 = np.sum(s2) #s2的总分数\n",
    "        s1SQ = np.sum(s1**2) #s1的平方和\n",
    "        s2SQ = np.sum(s2**2) #s2的平方和\n",
    "        pSum = np.sum(s1*s2) #s1* s2\n",
    "\n",
    "        #分子\n",
    "        num = pSum -(sum1* sum2/n)\n",
    "        #分母 \n",
    "        den = np.sqrt((s1SQ**2 - sum1**2/n) * (s2SQ**2 - sum2**2/n))\n",
    "        if den==0:\n",
    "            return 0.0;\n",
    "        else:\n",
    "            return num/den\n",
    "        \n",
    "    \n",
    "\n",
    "  def userCFReco(self, userId, eventId):\n",
    "        \"\"\"\n",
    "        根据User-based协同过滤，得到event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i\n",
    "          for every other user v that has a preference for i\n",
    "            compute similarity s between u and v\n",
    "            incorporate v's preference for i weighted by s into running aversge\n",
    "        return top items ranked by weighted average\n",
    "        \"\"\"\n",
    "        #请补充完整代码\n",
    "        ans = 0.0\n",
    "        sim_accoumulate =0.0 #分母 相似度累积\n",
    "        num =0.0 #分子\n",
    "        #1获取用户,和事件\n",
    "        u = self.userIndex[userId]\n",
    "        e = self.eventIndex[eventId]\n",
    "        #遍历活动寻找相关用户\n",
    "        for user in self.usersForItem[e]:\n",
    "            sim = self.sim_cal_UserCF(uid1=user, uid2=u)\n",
    "            if sim==0:\n",
    "                continue\n",
    "\n",
    "            num += sim * self.userEventScores[user,e]\n",
    "            sim_accoumulate +=sim\n",
    "\n",
    "        if sim_accoumulate==0:\n",
    "            return 0.0;\n",
    "        ans = num/sim_accoumulate\n",
    "\n",
    "        #输出在0到1之间\n",
    "        if ans>1:\n",
    "            return 1\n",
    "        elif ans <= 0:\n",
    "            return 0\n",
    "        return ans\n",
    "\n",
    "\n",
    "  def sim_cal_ItemCF(self, i_id1, i_id2):\n",
    "    #计算Item i_id1和i_id2之间的相似性\n",
    "    #请补充完整代码\n",
    "        similarity = 0.0 #相似度，初始为0\n",
    "        userList = {}\n",
    "        for user in self.usersForItem[i_id1]: #找出活动参加的所有用户\n",
    "            if user in self.usersForItem[i_id2]: #用户参加了这两个活动\n",
    "                userList[user]=1 #加入集合\n",
    "        \n",
    "        n = len(userList)\n",
    "        if(n==0):\n",
    "            return similarity\n",
    "       \n",
    "        #用户1 打过分的所有有效item \n",
    "        s1 = np.array([self.userEventScores[u,i_id1] for u in userList])\n",
    "        \n",
    "         #用户2 打过分的所有有效item \n",
    "        s2 = np.array([self.userEventScores[u,i_id2] for u in userList])\n",
    "        \n",
    "        #s1和s2相当于一个矩阵了可以使用算法了\n",
    "        sum1 = np.sum(s1) #s1的总分数\n",
    "        sum2 = np.sum(s2) #s2的总分数\n",
    "        s1SQ = np.sum(s1**2) #s1的平方和\n",
    "        s2SQ = np.sum(s2**2) #s2的平方和\n",
    "        pSum = np.sum(s1*s2) #s1* s2\n",
    "\n",
    "        #分子\n",
    "        num = pSum -(sum1* sum2/n)\n",
    "        #分母 \n",
    "        den = np.sqrt((s1SQ**2 - sum1**2/n) * (s2SQ**2 - sum2**2/n))\n",
    "        if den==0:\n",
    "            return 0.0;\n",
    "        else:\n",
    "            return num/den\n",
    "            \n",
    "  def eventCFReco(self, userId, eventId):    \n",
    "        \"\"\"\n",
    "        根据基于物品的协同过滤，得到Event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i \n",
    "            for every item j tht u has a preference for\n",
    "                compute similarity s between i and j\n",
    "                add u's preference for j weighted by s to a running average\n",
    "        return top items, ranked by weighted average\n",
    "        \"\"\"\n",
    "      #请补充完整代码\n",
    "        ans = 0.0\n",
    "        sim_accoumulate =0.0 #分母 相似度累积\n",
    "        num =0.0 #分子\n",
    "        #1获取用户,和事件\n",
    "        u = self.userIndex[userId]\n",
    "        e = self.eventIndex[eventId]\n",
    "        #遍历用户寻找相关活动\n",
    "        for event in self.itemsForUser[u]:\n",
    "            sim = self.sim_cal_ItemCF(i_id1=event, i_id2=e)\n",
    "            if sim==0:continue\n",
    "\n",
    "            num += sim * self.userEventScores[u,event]\n",
    "            sim_accoumulate +=sim\n",
    "\n",
    "        if sim_accoumulate==0:\n",
    "            return 0.0;\n",
    "        ans = num/sim_accoumulate\n",
    "\n",
    "        #输出在0到1之间\n",
    "        if ans>1:\n",
    "            return 1\n",
    "        elif ans<0:\n",
    "            return 0\n",
    "        return ans\n",
    "\n",
    "\n",
    "  def svdCFReco(self, userId, eventId):\n",
    "    #基于模型的协同过滤, SVD++/LFM\n",
    "    u = self.userIndex[userId]\n",
    "    i = self.eventIndex[eventId]\n",
    "\n",
    "    return self.pred_SVD(u,i)\n",
    "\n",
    "  def userReco(self, userId, eventId):\n",
    "    \"\"\"\n",
    "    类似基于User-based协同过滤，只是用户之间的相似度由用户本身的属性得到，计算event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i\n",
    "      for every other user v that has a preference for i\n",
    "        compute similarity s between u and v\n",
    "        incorporate v's preference for i weighted by s into running aversge\n",
    "    return top items ranked by weighted average\n",
    "    \"\"\"\n",
    "    i = self.userIndex[userId]\n",
    "    j = self.eventIndex[eventId]\n",
    "\n",
    "    vs = self.userEventScores[:, j]\n",
    "    sims = self.userSimMatrix[i, :]\n",
    "\n",
    "    prod = sims * vs\n",
    "\n",
    "    try:\n",
    "      return prod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      return 0\n",
    "\n",
    "  def eventReco(self, userId, eventId):\n",
    "    \"\"\"\n",
    "    类似基于Item-based协同过滤，只是item之间的相似度由item本身的属性得到，计算Event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i \n",
    "      for every item j that u has a preference for\n",
    "        compute similarity s between i and j\n",
    "        add u's preference for j weighted by s to a running average\n",
    "    return top items, ranked by weighted average\n",
    "    \"\"\"\n",
    "    i = self.userIndex[userId]\n",
    "    j = self.eventIndex[eventId]\n",
    "    js = self.userEventScores[i, :]\n",
    "    psim = self.eventPropSim[:, j]\n",
    "    csim = self.eventContSim[:, j]\n",
    "    pprod = js * psim\n",
    "    cprod = js * csim\n",
    "    \n",
    "    pscore = 0\n",
    "    cscore = 0\n",
    "    try:\n",
    "      pscore = pprod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      pass\n",
    "    try:\n",
    "      cscore = cprod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      pass\n",
    "    return pscore, cscore\n",
    "\n",
    "  def userPop(self, userId):\n",
    "    \"\"\"\n",
    "    基于用户的朋友个数来推断用户的社交程度\n",
    "    主要的考量是如果用户的朋友非常多，可能会更倾向于参加各种社交活动\n",
    "    \"\"\"\n",
    "    if userId in self.userIndex.keys():\n",
    "      i = self.userIndex[userId]\n",
    "      try:\n",
    "        return self.numFriends[0, i]\n",
    "      except IndexError:\n",
    "        return 0\n",
    "    else:\n",
    "      return 0\n",
    "\n",
    "  def friendInfluence(self, userId):\n",
    "    \"\"\"\n",
    "    朋友对用户的影响\n",
    "    主要考虑用户所有的朋友中，有多少是非常喜欢参加各种社交活动/event的\n",
    "    用户的朋友圈如果都积极参与各种event，可能会对当前用户有一定的影响\n",
    "    \"\"\"\n",
    "    nusers = np.shape(self.userFriends)[1]\n",
    "    i = self.userIndex[userId]\n",
    "    return (self.userFriends[i, :].sum(axis=0) / nusers)[0,0]\n",
    "\n",
    "  def eventPop(self, eventId):\n",
    "    \"\"\"\n",
    "    本活动本身的热度\n",
    "    主要是通过参与的人数来界定的\n",
    "    \"\"\"\n",
    "    i = self.eventIndex[eventId]\n",
    "    return self.eventPopularity[i, 0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateRSData(RS, train=True, header=True):\n",
    "    \"\"\"\n",
    "    把前面user-based协同过滤 和 item-based协同过滤，以及各种热度和影响度作为特征组合在一起\n",
    "    生成新的训练数据，用于分类器分类使用\n",
    "    \"\"\"\n",
    "    fn = \"train.csv\" if train else \"test.csv\"\n",
    "    fin = open(\"./data/\"+fn, 'r')\n",
    "    fout = open(\"./data/data_\"+fn, 'w')\n",
    "    \n",
    "    #忽略第一行（列名字）\n",
    "    fin.readline().strip().split(\",\")\n",
    "    \n",
    "    # write output header\n",
    "    if header:\n",
    "      ocolnames = [\"invited\", \"userCF_reco\", \"evtCF_reco\",\"svdCF_reco\",\"user_reco\", \"evt_p_reco\",\n",
    "        \"evt_c_reco\", \"user_pop\", \"frnd_infl\", \"evt_pop\"]\n",
    "      if train:\n",
    "        ocolnames.append(\"interested\")\n",
    "        ocolnames.append(\"not_interested\")\n",
    "      fout.write(\",\".join(ocolnames) + \"\\n\")\n",
    "    \n",
    "    ln = 0\n",
    "    for line in fin:\n",
    "      ln += 1\n",
    "      if ln%500 == 0:\n",
    "          print (\"%s:%d (userId, eventId)=(%s, %s)\" % (fn, ln, userId, eventId))\n",
    "          #break;\n",
    "      \n",
    "      cols = line.strip().split(\",\")\n",
    "      userId = cols[0]\n",
    "      eventId = cols[1]\n",
    "      invited = cols[2]\n",
    "      \n",
    "      userCF_reco = RS.userCFReco(userId, eventId)\n",
    "      itemCF_reco = RS.eventCFReco(userId, eventId)\n",
    "      svdCF_reco = RS.svdCFReco(userId, eventId)\n",
    "        \n",
    "      user_reco = RS.userReco(userId, eventId)\n",
    "      evt_p_reco, evt_c_reco = RS.eventReco(userId, eventId)\n",
    "      user_pop = RS.userPop(userId)\n",
    "     \n",
    "      frnd_infl = RS.friendInfluence(userId)\n",
    "      evt_pop = RS.eventPop(eventId)\n",
    "      ocols = [invited, userCF_reco, itemCF_reco, svdCF_reco,user_reco, evt_p_reco,\n",
    "        evt_c_reco, user_pop, frnd_infl, evt_pop]\n",
    "      \n",
    "      if train:\n",
    "        ocols.append(cols[4]) # interested\n",
    "        ocols.append(cols[5]) # not_interested\n",
    "      fout.write(\",\".join(map(lambda x: str(x), ocols)) + \"\\n\")\n",
    "    \n",
    "    fin.close()\n",
    "    fout.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVD Train...\n",
      "the  0 -th  step is running\n",
      "the  1 -th  step is running\n",
      "the  2 -th  step is running\n",
      "the  3 -th  step is running\n",
      "the  4 -th  step is running\n",
      "the  5 -th  step is running\n",
      "the  6 -th  step is running\n",
      "the  7 -th  step is running\n",
      "the  8 -th  step is running\n",
      "the  9 -th  step is running\n",
      "the  10 -th  step is running\n",
      "the  11 -th  step is running\n",
      "the  12 -th  step is running\n",
      "the  13 -th  step is running\n",
      "the  14 -th  step is running\n",
      "the  15 -th  step is running\n",
      "the  16 -th  step is running\n",
      "the  17 -th  step is running\n",
      "the  18 -th  step is running\n",
      "the  19 -th  step is running\n",
      "the  20 -th  step is running\n",
      "the  21 -th  step is running\n",
      "the  22 -th  step is running\n",
      "the  23 -th  step is running\n",
      "the  24 -th  step is running\n",
      "the  25 -th  step is running\n",
      "the  26 -th  step is running\n",
      "the  27 -th  step is running\n",
      "the  28 -th  step is running\n",
      "the  29 -th  step is running\n",
      "the  30 -th  step is running\n",
      "the  31 -th  step is running\n",
      "the  32 -th  step is running\n",
      "the  33 -th  step is running\n",
      "the  34 -th  step is running\n",
      "the  35 -th  step is running\n",
      "the  36 -th  step is running\n",
      "the  37 -th  step is running\n",
      "the  38 -th  step is running\n",
      "the  39 -th  step is running\n",
      "the  40 -th  step is running\n",
      "the  41 -th  step is running\n",
      "the  42 -th  step is running\n",
      "the  43 -th  step is running\n",
      "the  44 -th  step is running\n",
      "the  45 -th  step is running\n",
      "the  46 -th  step is running\n",
      "the  47 -th  step is running\n",
      "the  48 -th  step is running\n",
      "the  49 -th  step is running\n",
      "the  50 -th  step is running\n",
      "the  51 -th  step is running\n",
      "the  52 -th  step is running\n",
      "the  53 -th  step is running\n",
      "the  54 -th  step is running\n",
      "the  55 -th  step is running\n",
      "the  56 -th  step is running\n",
      "the  57 -th  step is running\n",
      "the  58 -th  step is running\n",
      "the  59 -th  step is running\n",
      "the  60 -th  step is running\n",
      "the  61 -th  step is running\n",
      "the  62 -th  step is running\n",
      "the  63 -th  step is running\n",
      "the  64 -th  step is running\n",
      "the  65 -th  step is running\n",
      "the  66 -th  step is running\n",
      "the  67 -th  step is running\n",
      "the  68 -th  step is running\n",
      "the  69 -th  step is running\n",
      "the  70 -th  step is running\n",
      "the  71 -th  step is running\n",
      "the  72 -th  step is running\n",
      "the  73 -th  step is running\n",
      "the  74 -th  step is running\n",
      "the  75 -th  step is running\n",
      "the  76 -th  step is running\n",
      "the  77 -th  step is running\n",
      "the  78 -th  step is running\n",
      "the  79 -th  step is running\n",
      "the  80 -th  step is running\n",
      "the  81 -th  step is running\n",
      "the  82 -th  step is running\n",
      "the  83 -th  step is running\n",
      "the  84 -th  step is running\n",
      "the  85 -th  step is running\n",
      "the  86 -th  step is running\n",
      "the  87 -th  step is running\n",
      "the  88 -th  step is running\n",
      "the  89 -th  step is running\n",
      "the  90 -th  step is running\n",
      "the  91 -th  step is running\n",
      "the  92 -th  step is running\n",
      "the  93 -th  step is running\n",
      "the  94 -th  step is running\n",
      "the  95 -th  step is running\n",
      "the  96 -th  step is running\n",
      "the  97 -th  step is running\n",
      "the  98 -th  step is running\n",
      "the  99 -th  step is running\n",
      "SVD trained\n",
      "生成训练数据...\n",
      "\n",
      "train.csv:500 (userId, eventId)=(123290209, 1887085024)\n",
      "train.csv:1000 (userId, eventId)=(272886293, 199858305)\n",
      "train.csv:1500 (userId, eventId)=(395305791, 1582270949)\n",
      "train.csv:2000 (userId, eventId)=(527523423, 3272728211)\n",
      "train.csv:2500 (userId, eventId)=(651258472, 792632006)\n",
      "train.csv:3000 (userId, eventId)=(811791433, 524756826)\n",
      "train.csv:3500 (userId, eventId)=(985547042, 1269035551)\n",
      "train.csv:4000 (userId, eventId)=(1107615001, 173949238)\n",
      "train.csv:4500 (userId, eventId)=(1236336671, 3849306291)\n",
      "train.csv:5000 (userId, eventId)=(1414301782, 2652356640)\n",
      "train.csv:5500 (userId, eventId)=(1595465532, 955398943)\n",
      "train.csv:6000 (userId, eventId)=(1747091728, 2131379889)\n",
      "train.csv:6500 (userId, eventId)=(1914182220, 955398943)\n",
      "train.csv:7000 (userId, eventId)=(2071842684, 1076364848)\n",
      "train.csv:7500 (userId, eventId)=(2217853337, 3051438735)\n",
      "train.csv:8000 (userId, eventId)=(2338481531, 2525447278)\n",
      "train.csv:8500 (userId, eventId)=(2489551967, 520657921)\n",
      "train.csv:9000 (userId, eventId)=(2650493630, 87962584)\n",
      "train.csv:9500 (userId, eventId)=(2791418962, 4223848259)\n",
      "train.csv:10000 (userId, eventId)=(2903662804, 2791462807)\n",
      "train.csv:10500 (userId, eventId)=(3036141956, 3929507420)\n",
      "train.csv:11000 (userId, eventId)=(3176074542, 3459485614)\n",
      "train.csv:11500 (userId, eventId)=(3285425249, 2271782630)\n",
      "train.csv:12000 (userId, eventId)=(3410667855, 1063772489)\n",
      "train.csv:12500 (userId, eventId)=(3531604778, 2584839423)\n",
      "train.csv:13000 (userId, eventId)=(3686871863, 53495098)\n",
      "train.csv:13500 (userId, eventId)=(3833637800, 2415873572)\n",
      "train.csv:14000 (userId, eventId)=(3944021305, 2096772901)\n",
      "train.csv:14500 (userId, eventId)=(4075466480, 3567240505)\n",
      "train.csv:15000 (userId, eventId)=(4197193550, 1628057176)\n",
      "生成预测数据...\n",
      "\n",
      "test.csv:500 (userId, eventId)=(182290053, 2529072432)\n",
      "test.csv:1000 (userId, eventId)=(433510318, 4244463632)\n",
      "test.csv:1500 (userId, eventId)=(632808865, 2845303452)\n",
      "test.csv:2000 (userId, eventId)=(813611885, 2036538169)\n",
      "test.csv:2500 (userId, eventId)=(1010701404, 303459881)\n",
      "test.csv:3000 (userId, eventId)=(1210932037, 2529072432)\n",
      "test.csv:3500 (userId, eventId)=(1452921099, 2705317682)\n",
      "test.csv:4000 (userId, eventId)=(1623287180, 1626678328)\n",
      "test.csv:4500 (userId, eventId)=(1855201342, 2603032829)\n",
      "test.csv:5000 (userId, eventId)=(2083900381, 2529072432)\n",
      "test.csv:5500 (userId, eventId)=(2318415276, 2509151803)\n",
      "test.csv:6000 (userId, eventId)=(2528161539, 4025975316)\n",
      "test.csv:6500 (userId, eventId)=(2749110768, 4244406355)\n",
      "test.csv:7000 (userId, eventId)=(2927772127, 1532377761)\n",
      "test.csv:7500 (userId, eventId)=(3199685636, 1776393554)\n",
      "test.csv:8000 (userId, eventId)=(3393388475, 680270887)\n",
      "test.csv:8500 (userId, eventId)=(3601169721, 154434302)\n",
      "test.csv:9000 (userId, eventId)=(3828963415, 3067222491)\n",
      "test.csv:9500 (userId, eventId)=(4018723397, 2522610844)\n",
      "test.csv:10000 (userId, eventId)=(4180064266, 2658555390)\n"
     ]
    }
   ],
   "source": [
    "RS = RecommonderSystem()\n",
    "print (\"生成训练数据...\\n\")\n",
    "generateRSData(RS,train=True,  header=True)\n",
    "\n",
    "print (\"生成预测数据...\\n\")\n",
    "generateRSData(RS, train=False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间、地点等特征都没有处理了，可以考虑用户看到event的时间与event开始时间的差、用户地点和event地点的差异。。。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
